R Notebook
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source("./tianfengRwrappers.R")# plan("multiprocess",workers = 8)读取结果
human_coronary <- readRDS("human_coronary.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds") #已经经过分组处理了修改分群
umapplot(CA_dataset2, split.by = "sample")
umapplot(CA_dataset2,group.by = "groups", split.by = "conditions")
multi_featureplot(c("HEY1","GJA5","SEMA3G","CXCL12","SOX17","CDH5","PECAM1"),CA_dataset2)
multi_featureplot(c("ACKR1","PLVAP","ITGA6","PECAM1"),CA_dataset2)
# table(CA_dataset2$sample)
# 关注cluster 3-6 EC
# cluster 7 基质细胞
multi_featureplot(c("LYZ","PTPRC","CD69","EPCAM","CDH1","PDGFRB","COL1A2","PECAM1","CLDN5"),CA_dataset2)
Dotplot(c("LUM","MMP2","MGP","DCN","MYH11","ACTA2","CNN1","TAGLN"),CA_dataset2) #cluster 7 细胞可以被认为是modulated SMCs
CA_dataset2 <- AddModuleScore(CA_dataset2,list(c("LUM","MMP2","MGP","DCN")))
CA_dataset2 <- AddModuleScore(CA_dataset2,list(c("MYH11","ACTA2","CNN1","TAGLN")))
multi_featureplot(c("Cluster1","LUM","ACTA2","TAGLN"),CA_dataset2)
multi_featureplot(c("MMP2","GJA4","PECAM1","ACKR1"), CA_dataset2)
multi_featureplot(c("MMP2","GJA4","PECAM1","ACKR1"), human_coronary)
细胞分类
multi_featureplot(c("LYZ","PTPRC","CD69","PDGFRB","COL1A2","PECAM1","CLDN5"), CA_dataset1)
f("LUM",CA_dataset1)
umapplot(CA_dataset1)CA_dataset1 <- CA_dataset1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.5)Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 8709
Number of edges: 279176
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9035
Number of communities: 13
Elapsed time: 0 seconds
CA_dataset2_markers <- FindAllMarkers(CA_dataset2, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
Calculating cluster T cell
| | 0 % ~calculating
|+++ | 4 % ~01s
|+++++ | 9 % ~01s
|+++++++ | 13% ~01s
|+++++++++ | 17% ~01s
|+++++++++++ | 22% ~01s
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|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster NK cell
| | 0 % ~calculating
|+++ | 5 % ~01s
|+++++ | 10% ~01s
|++++++++ | 15% ~01s
|++++++++++ | 20% ~01s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster Neural progenitor
| | 0 % ~calculating
|+ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster Stromal cell
| | 0 % ~calculating
|+ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 7 % ~04s
|+++++ | 9 % ~04s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster EC
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~02s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster Macrophage
| | 0 % ~calculating
|+ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster B cell
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster Mast cell
| | 0 % ~calculating
|++ | 2 % ~01s
|+++ | 5 % ~01s
|++++ | 7 % ~01s
|+++++ | 10% ~01s
|++++++ | 12% ~01s
|++++++++ | 14% ~01s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s 基质细胞
ds0 <- readRDS("ds0.rds")
ds1 <- readRDS("ds1.rds")
ds2 <- readRDS("ds2.rds")去除边角的离群细胞
select.cells <- CellSelector(plot = DimPlot(ds0, reduction = "umap"))
Listening on http://127.0.0.1:3689
ds0 <- subset(ds0, cell = select.cells)
umapplot(ds0)
细分亚群
ds2_PA_markers <- FindAllMarkers(ds2_PA, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
Calculating cluster Fibromyocyte
| | 0 % ~calculating
|+++ | 6 % ~00s
|++++++ | 12% ~00s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster SMC1
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster SMC2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s 
write.csv(ds2_AC_markers,"ds2_AC_markers.csv")
write.csv(ds2_PA_markers,"ds2_PA_markers.csv")
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
source("./tianfengRwrappers.R")
# plan("multiprocess",workers = 8)
```

# 读取结果
```{r}
human_coronary <- readRDS("human_coronary.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds") #已经经过分组处理了
```

## 修改分群
```{r}
umapplot(CA_dataset2, split.by = "sample")
umapplot(CA_dataset2,group.by = "groups", split.by = "conditions")
multi_featureplot(c("HEY1","GJA5","SEMA3G","CXCL12","SOX17","CDH5","PECAM1"),CA_dataset2)
multi_featureplot(c("ACKR1","PLVAP","ITGA6","PECAM1"),CA_dataset2)
```

```{r}
# table(CA_dataset2$sample)

# 关注cluster 3-6 EC
# cluster 7 基质细胞

multi_featureplot(c("LYZ","PTPRC","CD69","EPCAM","CDH1","PDGFRB","COL1A2","PECAM1","CLDN5"),CA_dataset2)

Dotplot(c("LUM","MMP2","MGP","DCN","MYH11","ACTA2","CNN1","TAGLN"),CA_dataset2) #cluster 7 细胞可以被认为是modulated SMCs
CA_dataset2 <- AddModuleScore(CA_dataset2,list(c("LUM","MMP2","MGP","DCN")))
CA_dataset2 <- AddModuleScore(CA_dataset2,list(c("MYH11","ACTA2","CNN1","TAGLN")))
multi_featureplot(c("Cluster1","LUM","ACTA2","TAGLN"),CA_dataset2)
multi_featureplot(c("MMP2","GJA4","PECAM1","ACKR1"), CA_dataset2)
multi_featureplot(c("MMP2","GJA4","PECAM1","ACKR1"), human_coronary)

```
# 细胞分类
```{r}
multi_featureplot(c("LYZ","PTPRC","CD69","PDGFRB","COL1A2","PECAM1","CLDN5"), CA_dataset1)
f("LUM",CA_dataset1)
umapplot(CA_dataset1)
```

```{r}
CA_dataset1  <- CA_dataset1  %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.5)
```


```{r}
Idents(CA_dataset1) <- CA_dataset1$seurat_clusters
CA_dataset1 <- RenameIdents(CA_dataset1,'0' = 'Macrophage','1'='SMC','2' = 'SMC','3' = 'SMC','4' = 'NK cell',
                            '5' = 'EC1','6' = 'SMC','10'='Mast cell','8'='Macrophage','11'='Macrophage','12'='B cell','7'='EC1', '9'='EC2')
Idents(CA_dataset1) <- ordered(Idents(CA_dataset1),levels = c("B cell","EC1","EC2","Macrophage","Mast cell","NK cell","SMC"))
CA_dataset1$Classification1 <- Idents(CA_dataset1)
umapplot(CA_dataset1)
# saveRDS(CA_dataset1,"CA_dataset1.rds")

Idents(CA_dataset2) <- CA_dataset2$seurat_clusters
umapplot(CA_dataset2)
CA_dataset2 <- RenameIdents(CA_dataset2,'0' = 'T cell','4' = 'NK cell','9' = 'Neural progenitor','2' = 'Stromal cell','7'='Stromal cell','3'='EC','6'='EC','1'='Macrophage','5'='B cell','8'='Mast cell','10'='B cell')
Idents(CA_dataset2) <- ordered(Idents(CA_dataset2),levels = c("B cell",'EC','FB',"Macrophage","Mast cell",'Neural progenitor',"NK cell","SMC",'T cell'))
CA_dataset2$Classification1 <- Idents(CA_dataset2)
umapplot(CA_dataset2)

#saveRDS(CA_dataset2,"CA_dataset2.rds")

Idents(human_coronary) <- human_coronary$seurat_clusters
umapplot(human_coronary)
human_coronary <- RenameIdents(human_coronary,'0' = 'FB','1' = 'Macrophage','2'='EC','3'='SMC','4'='SMC','5' = 'T cell','6' = 'B cell','7'='Neuron','8'='Plasma','9'='Plasma','10'='EC')
saveRDS(human_coronary,"human_coronary.rds")
CA_dataset2$Classification1 <- Idents(CA_dataset2)
umapplot(CA_dataset2)
```

```{r}
CA_dataset1_markers <- FindAllMarkers(CA_dataset1, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
CA_dataset2_markers <- FindAllMarkers(CA_dataset2, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
human_coronary_markers <- FindAllMarkers(human_coronary, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
```


## 基质细胞
```{r}
ds0 <- readRDS("ds0.rds")
ds1 <- readRDS("ds1.rds")
ds2 <- readRDS("ds2.rds")
```

## 去除边角的离群细胞
```{r}
# select.cells <- CellSelector(plot = DimPlot(ds1, reduction = "umap")) 
# ds1 <- subset(ds1, cell = select.cells)
# umapplot(ds1)
# saveRDS(ds1,"ds1.rds")


# select.cells <- CellSelector(plot = DimPlot(ds0, reduction = "umap")) 
# ds0 <- subset(ds0, cell = select.cells)
# umapplot(ds0)
# saveRDS(ds0,"ds0.rds")
```

## 细分亚群
```{r}
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")

ds0  <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
ds1  <- ds1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)

ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.15)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

ds2_AC_markers <- FindAllMarkers(ds2_AC, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
ds2_PA_markers <- FindAllMarkers(ds2_PA, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
ds0markers <- FindAllMarkers(ds0, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
ds1markers <- FindAllMarkers(ds1, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)
ds2markers <- FindAllMarkers(ds2, logfc.threshold = 0.7, min.diff.pct = 0.3,only.pos = T)

umapplot(ds0)
umapplot(ds1)

umapplot(ds2_AC)
umapplot(ds2_PA)
#umapplot(ds2,split.by = "conditions",group.by = "Classification1")


Idents(ds0) <- ds0$seurat_clusters
ds0 <- RenameIdents(ds0,'0' = 'Fibroblast','1'='SMC','2' = 'Fibromyocyte','3' = 'Pericyte','4'='Pericyte','5'='Unknown')

umapplot(ds0)

Idents(ds1) <- ds1$seurat_clusters
ds1 <- RenameIdents(ds1,'0' = 'SMC1','1'='Fibromyocyte','2' = 'Unkown1','3' = 'SMC2','4'='Unknown2')

Idents(ds2) <- ds2$seurat_clusters
ds2 <- RenameIdents(ds2,'0' = 'SMC1','1'='Fibromyocyte','2' = 'SMC2','3' = 'FB','4'='SMC3')

umapplot(ds2)
umapplot(ds1)
```

```{r}
Idents(ds2_PA) <- ds2_PA$seurat_clusters
umapplot(ds2_PA)
ds2_PA <- RenameIdents(ds2_PA,'0'='Fibromyocyte','1' = 'SMC1','2' = 'SMC2')
saveRDS(ds2_PA, file = "ds2_PA.rds")

Idents(ds2_AC) <- ds2_AC$seurat_clusters
umapplot(ds2_AC)
ds2_AC <- RenameIdents(ds2_AC,'0'='Fibroblast','1' = 'SMC1','3' = 'SMC2','2' = 'Fibromyocyte')
Idents(ds2_AC) <- factor(Idents(ds2_AC),levels = c("Fibromyocyte","SMC1","SMC2","Fibroblast"))
saveRDS(ds2_AC, file = "ds2_AC.rds")

ggsave("ds2_PAumap.svg",plot = umapplot(ds2_PA),device = svg, width = 6, height = 4)
ggsave("ds2_ACumap.svg",plot = umapplot(ds2_AC),device = svg, width = 6, height = 4)
```

```{r}
write.csv(ds2_AC_markers,"ds2_AC_markers.csv")
write.csv(ds2_PA_markers,"ds2_PA_markers.csv")

```

